Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA315538 | ENA |
REPOSITORIES: ENA
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Project description:Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel measurements across thousands of genes and gene products. Such high-throughput technologies have been extensively used to carry out genome-wide studies particularly in the context of diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome, and proteome of a single mammalian cell type to obtain a coherent view of the complex interplay between omes has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells, revealing hundreds of unannotated mRNA transcripts, miRNAs, pseudogenes, and noncoding RNAs. Additionally, we carried out a comparative analysis of naïve CD4+ T cells with primary resting memory CD4+ T cells, which have provided novel insights into T cell biology. Overall, our data will serve as a baseline reference of a single pure population of cells for future systems level analysis of other defined cell populations.
2015-11-12 | PXD000376 | Pride
Project description:Cellular barcoding using heritable synthetic barcodes coupled to high throughput sequencing is a powerful technique for the accurate tracing of clonal lineages in a wide variety of biological contexts. Recent studies have integrated cellular barcoding with a single-cell transcriptomics readout, extending the capabilities of these lineage tracing methods to the single-cell level. However there remains a lack of scalable and standardised open-source tools to pre-process and visualise both bulk and single-cell level cellular barcoding datasets. Here, we describe bartools, an open-source R-based toolkit that streamlines the pre-processing, analysis and visualisation of synthetic cellular barcoding datasets. In addition, we developed BARtab, a portable and scalable Nextflow pipeline that automates upstream barcode extraction, quality control, filtering and enumeration from high throughput sequencing data. In addition to population-level cellular barcoding datasets, BARtab and bartools contain methods for the extraction, annotation, and visualisation of transcribed barcodes from single-cell RNA-seq and spatial transcriptomics experiments, thus extending the analytical toolbox to also support novel expressed cellular barcoding methodologies. We showcase the integrated BARtab and bartools workflow through the analysis of bulk, single-cell, and spatial transcriptomics cellular barcoding datasets.
2023-11-05 | GSE246611 | GEO
Project description:We generated single-cell transcriptomes from a large number of single cells using several commercially available platforms, in both microliter and nanoliter volumes, and compared performance between them. We benchmarked each method to conventional RNA-seq of the same sample using bulk total RNA, as well as to multiplexed qPCR, which is the current gold standard for quantitative single-cell gene expression analysis. In doing so, we were able to systematically evaluate the sensitivity, precision, and accuracy of various approaches to single-cell RNA-seq. Our results show that it is possible to use single-cell RNA-seq to perform quantitative transcriptome measurements of individual cells, that it is possible to obtain quantitative and accurate gene expression measurements with a relatively small number of sequencing reads, and that when such measurements are performed on large numbers of cells, one can recapitulate the bulk transcriptome complexity, and the distributions of gene expression levels found by single-cell qPCR. 109 single-cell human transcriptomes were analyzed in total; 96 using nanoliter volume sample processing on a microfluidic platform, Nextera library prep (biological replicates); 3 using the SMARTer cDNA synthesis kit, Nextera library prep (biological replicates); 3 using the Transplex cDNA synthesis kit, Nextera library prep (biological replicates); 7 using the Ovation Nugen cDNA synthesis kit (biological replicates) where 3 used Nextera library prep and 4 used NEBNext library prep. In addition, 4 bulk RNA samples were sequenced: bulk RNA generated using ~1 million pooled cells was used to make bulk libraries, 2 of which were made using SMARTer cDNA synthesis kit (technical replicates) and 2 made using Superscript RT kit with no amplification (technical replicates). All 4 bulk samples were made into libraries using Nextera.
2013-10-20 | E-GEOD-51254 | biostudies-arrayexpress
Project description:A time course of the macrophage response to Salmonella exposure analyzing the effects of input cell number as a control for single cell studies Mouse macrophages were exposed to Salmonella enterica for different lengths of time. Libraries were constructed using either approximately 500,00 macrophages lysed directly on a tissue culture dish (bulk) or using only 150 cells isolated using FACS (sorted). All libraries were constructed in duplicate (bulk) or triplicate (sorted). All replicates are biological replicates
2015-09-07 | E-GEOD-65530 | biostudies-arrayexpress
Project description:In response to antigen challenge, human B cells clonally expand, undergo selection and differentiate within secondary lymphoid tissues to produce mature B cell subsets and high affinity antibodies necessary for an effective immune response. However, the interplay between affinity, antibody class and different B cell fates has proved challenging to decipher in primary human tissue. We have applied an integrated analysis of bulk and single-cell antibody repertoires paired with single-cell transcriptomics of human B cells from a model secondary lymphoid tissue. Specifically, here we have performed bulk B cell repertoire sequencing of the immunoglobulin heavy chain (IgH) for sorted B cell subsets from paediatric tonsil tissue. Matched single-cell gene expression and single-cell VDJ data are also available for the same patient donors.
2020-05-01 | E-MTAB-8999 | biostudies-arrayexpress
Project description:We previously identified a myeloid cell subset expressing the markers CD88 and CD317 upon induction of inflammation in a mouse model of CNS autoimmunity. Here, we use single cell transcriptomics and epigenomics to extensively phenotype these cells and determine their compartment of origin.
2022-12-30 | GSE210230 | GEO
Project description:We previously identified a myeloid cell subset expressing the markers CD88 and CD317 upon induction of inflammation in a mouse model of CNS autoimmunity. Here, we use single cell transcriptomics and epigenomics to extensively phenotype these cells and determine their compartment of origin.
2022-12-30 | GSE210209 | GEO
Project description:Cell-to-cell expression variation is a prevalent feature of cell populations, but the environmental, cellular and biochemical conditions under which variability in the level of one gene can or cannot be propagated to affect that of other genes in the underlying network within cells remain poorly understood. Here we explore this issue using single-cell qPCR and bulk-level RNAseq, CAGE, and ChIPseq analysis of human macrophages exposed to different cytokine environments. Our analyses reveal that cellular adaptation to different environments could involve tuning the extent of such variability (or information) propagation (IP) in the network, thereby shaping environment-dependent patterns of gene-gene correlations and phenotypic heterogeneity across single cells. We find that IP can be regulated through modification of multiple network and signaling parameters, such as the degree of transcription factor-chromatin interactions. Our findings thus suggest that adaptive IP may be a widespread, yet underexplored, aspect of cellular adaptation to distinct environments.
2017-03-27 | GSE80726 | GEO
Project description:Cell-to-cell expression variation is a prevalent feature of cell populations, but the environmental, cellular and biochemical conditions under which variability in the level of one gene can or cannot be propagated to affect that of other genes in the underlying network within cells remain poorly understood. Here we explore this issue using single-cell qPCR and bulk-level RNAseq, CAGE, and ChIPseq analysis of human macrophages exposed to different cytokine environments. Our analyses reveal that cellular adaptation to different environments could involve tuning the extent of such variability (or information) propagation (IP) in the network, thereby shaping environment-dependent patterns of gene-gene correlations and phenotypic heterogeneity across single cells. We find that IP can be regulated through modification of multiple network and signaling parameters, such as the degree of transcription factor-chromatin interactions. Our findings thus suggest that adaptive IP may be a widespread, yet underexplored, aspect of cellular adaptation to distinct environments.
2017-03-27 | GSE80720 | GEO
Project description:Single-cell epigenomics maps the continuous regulatory landscape of human hematopoietic differentiation [bulk RNA-Seq]
| PRJNA379785 | ENA